Now showing items 1-5 of 5
A Probabilistic Approach to Robust Shape Matching and Part Decomposition
We present a probabilistic approach to shape matching which is invariant to rotation, translation and scaling. Shapes are represented by unlabeled point sets, so discontinuous boundaries and non-boundary points do not ...
Kernel Carpentry for Online Regression using Randomly Varying Coefficient Model
We present a Bayesian formulation of locally weighted learning (LWL) using the novel concept of a randomly varying coefficient model. Based on this
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
For stationary systems, efficient techniques for adaptive motor control exist which learn the system’s inverse dynamics online and use this single model for control. However, in realistic domains the system dynamics often ...
Part-based Probabilistic Point Matching using Equivalence Constraints
Correspondence algorithms typically struggle with shapes that display part-based variation. We present a probabilistic approach that matches shapes using independent part transformations, where the parts themselves are ...
Learning Utility Surfaces for Movement Selection
Humanoid robots are highly redundant systems with respect to the tasks they are asked to perform. This redundancy manifests itself in the number of degrees of freedom of the robot exceeding the dimensionality of the ...